"""simple docstring"""



import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
   from PIL import Image
else:






   class  snake_case__      :


    @staticmethod
    def 						a__   ( *lowerCamelCase    ,		**lowerCamelCase						):
           pass






@is_pipeline_test
@require_torch
@require_vision
class  snake_case__      (       unittest.TestCase       ):
 _snake_case       :					Dict							  =						MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    pipeline("visual-question-answering"    ,		model="hf-internal-testing/tiny-vilt-random-vqa"						)
        __a       =    [
            {
                "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						),
                "question": "How many cats are there?",
            },
            {
                "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                "question": "How many cats are there?",
            },
        ]
        return vqa_pipeline, examples


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    vqa_pipeline(lowerCamelCase    ,		top_k=1						)
        self.assertEqual(
            lowerCamelCase    ,		[
                [{"score": ANY(lowerCamelCase						), "answer": ANY(lowerCamelCase						)}],
                [{"score": ANY(lowerCamelCase						), "answer": ANY(lowerCamelCase						)}],
            ]    ,		)


 @require_torch
 def 						a__   ( self						):
        __a       =    pipeline("visual-question-answering"    ,		model="hf-internal-testing/tiny-vilt-random-vqa"						)
        __a       =    "./tests/fixtures/tests_samples/COCO/000000039769.png"
        __a       =    "How many cats are there?"

        __a       =    vqa_pipeline(image=lowerCamelCase    ,		question="How many cats are there?"    ,		top_k=2						)
        self.assertEqual(
            lowerCamelCase    ,		[{"score": ANY(lowerCamelCase						), "answer": ANY(lowerCamelCase						)}, {"score": ANY(lowerCamelCase						), "answer": ANY(lowerCamelCase						)}]						)

        __a       =    vqa_pipeline({"image": image, "question": question}    ,		top_k=2						)
        self.assertEqual(
            lowerCamelCase    ,		[{"score": ANY(lowerCamelCase						), "answer": ANY(lowerCamelCase						)}, {"score": ANY(lowerCamelCase						), "answer": ANY(lowerCamelCase						)}]						)


 @slow
 @require_torch
 def 						a__   ( self						):
        __a       =    pipeline("visual-question-answering"    ,		model="dandelin/vilt-b32-finetuned-vqa"						)
        __a       =    "./tests/fixtures/tests_samples/COCO/000000039769.png"
        __a       =    "How many cats are there?"

        __a       =    vqa_pipeline(image=lowerCamelCase    ,		question=lowerCamelCase    ,		top_k=2						)
        self.assertEqual(
            nested_simplify(lowerCamelCase    ,		decimals=4						)    ,		[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]						)

        __a       =    vqa_pipeline({"image": image, "question": question}    ,		top_k=2						)
        self.assertEqual(
            nested_simplify(lowerCamelCase    ,		decimals=4						)    ,		[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]						)

        __a       =    vqa_pipeline(
            [{"image": image, "question": question}, {"image": image, "question": question}]    ,		top_k=2						)
        self.assertEqual(
            nested_simplify(lowerCamelCase    ,		decimals=4						)    ,		[[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2    ,		)






 @require_tf
 @unittest.skip("Visual question answering not implemented in TF"						)
 def 						a__   ( self						):
        pass

"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
